Various studies and experiments have demonstrated that conventional frameworks such as Shannon theory [1]}, detection theory [2]}, and queuing theory [3]} based wireless PHY suffer from performance degradation due to randomness and diversity of wireless environments. Instead of exploring approximate frameworks, recent advances in AI-MDL approaches and their ability to address hard-to-model problems offer a good alternative for making the wireless PHY robust [4]} [5]}. Numerous works have shown that traditional ML techniques have been successful in combating complex wireless environments and hardware non-linearities [6]}. However, they need manual feature selection. The DL approaches offer the capability to extract features from the data itself, thereby eliminating the need for manual feature extraction. This has lead to numerous AI-MDL based approaches for various problems such as modulation classification [7]}, [8]}, direction-of-arrival estimation [9]} [10]}, spectrum sensing [11]}, multiple-input multiple-output (MIMO) high-resolution channel feedback [12]} [13]}, mobility and beam management [14]}, link adaptation [15]}, localization [16]}. With the evolution of heterogeneous large-scale networks, existing frameworks suffer from scaling issues due to the high cost of exhaustive searching and iterative heuristic algorithms [17]} [18]}. Such scaling issues can be handled more efficiently using AI-MDL approaches [19]} [20]}. To bring DL-aided intelligent and reconfigurable wireless PHY into reality, issues such as potential use cases, achievable gain and complexity trade-off, evaluation methodologies, dataset availability, and standard compatibility impact need to be addressed [21]}, [6]}, [23]}, [24]}, [25]}.
